Why do I need this?

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US Presidential Policy puts the United States on an aggressive trajectory for returning to the moon by 2022.  COVID-19 has inhibited traditional working practices for many aerospace companies. Redmoon Systems aims to provide a consulting force capable of filling in the necessary gaps to enable America to achieve the Presidential goals in space.

What are we selling?

Our team has a history of innovation within the US Space program. We have staffed programs for the Department of Defense as well as NASA at companies such as Lockheed Martin, Raytheon, Boeing, and Northrop Grumman. Some examples of technologies pioneered by our engineers are

  • Advanced radar tracking algorithms for the F-18 aircraft
  • Infrared on-orbit cameras for NASA missions
  • Missile and re-entry vehicle tracking systems and technologies,
  • Space debris detection tracking and removal technologies
  • Many others

Our team is standing by to enable you to meet your aerospace and defense research goals.

Reviewing Proposals for Commercial Space Partnerships

Redmoon Systems is following current developements with the Air Education and Training Command headquarters, where we are forming a proposal effort by the aerospace industry on commercial space partnerships. Some of the topics included on-orbit manufacturing and repair, propulsion and maneuvering, ground infrastrcture components, power collection, storage, and transmission, space domain awareness, in-space data centers, and modular space robotics.

We see our role as providing leadership in the technologies which will promote battle space logistics and advances in space exploration and resource utilization.

Using Mars to inspect the asteroid belt

The main asteroid belt encircles the inner solar system. Roughly 3.2 AU from the sun, it is thought that the asteroid belt was not formed from by a planetary collision because the total combined mass of all asteroids is far too small (not even as much as Earth’s moon).

At Redmoon Systems, our intent is to detect, track and characterize the objects in the belt using infrared sensing technologies. One of these technologies is known as Firecat Observatory, Mars Mission.

Firecat is a passive sensor system located on the surface of Mars. Miniaturized and remotely deployed, the sensor consists of a single aperture through which an infrared multi-spectral detector array can detect light reflected from the asteroids nearest to Mars.

Inspired by a NASA review panel led by Jessy Cowan Sharp of NASA Ames, our team is running simulations to determine the best configuration for Firecat Observatory Mars Mission. Factors to be considered are the number of pixels, the pointing system, the power source, and transmitter configuration.

 

According to our initial design study, we have determined that the signal levels from three different rings or locations with the asteroid belt will be approximately:

The low signal levels require a more sensitive detector than expected, as compared to our moon-based debris observation mission.

 

The following figure shows the location and field depth of the main asteroid belt.

Manned Missions such as the Mars One mission plan to place humans on Mars by 2033. The presence of humans will only further the prospects of the Firecat Mars Observatory.

How to apply quantum principles to infrared tech

[latexpage]

When we learn physics in school, it is difficult to understand how to represent a falling ball as a math problem whose solution describes the real world. The first method we learned was Newtonian mechanics which is where everyone draws a force diagram, little arrows representing how a flower wants to evolve.

In college, we may learn something called Lagrangian mechanics which is a little different. For instance, instead of modeling forces, we talk about energy. Energy is often easier to find because it is always mass times velocity squared.

Lagrangian mechanics allows you to solve complex problems easily because you never need to know the forces at work.

But there is a trick to it. It requires identifying something called generalized coordinates, which can sometimes be challenging.

Actually, the invisible science behind Lagrangian mechanics revolves around a method or process known as the Calculus of Variations.

 

This involves two ideas: 1: that the differential equations of motion governing the dynamics of a classical or mechanical system can be deduced from a cost function and the fact that nature seeks to minimize the integral of this cost function. 2: that the way to identify the equations of motion require that we make small, even infinitesimal, changes in our path through the solution space.

In classical mechanics, the cost function has to do with energy minimization along a solution curve. Specifically, the cost function (Lagrangian) is defined on the tangent bundle (i.e. set of all tangent spaces to the solution curve, which contains velocity vectors). In Euclidean geometry, the “cost” function is actually the Euclidean distance, and is a metric defined on the solution curve or manifold itself (not the tangent bundle). The solution to problem in classical mechanics is a geodesic curve, i.e. one which minimizes the Lagrangian cost function.

In other domains, such as the infrared, it may be possible to construct a cost function based on the least action principle. However we must expand our notion of cost function.

 

       Greybody Curves

In the example below, the epsilon parameter is a constant between 0 and 1 for each colored curve. Epsilon determines the scale of the effective blackbody curve, which is represented by the value epsilon = 1.

In the absence of any experimental data on the thermal radiator, all values of epsilon are equally likely. One may think of the radiator in as existing (from our perspective) in an undetermined state. By analogy to the Everett interpretation of coherent quantum states, unique versions of the radiator can be said to exist in separated realities. When an experimental observation of the radiator is made, a specific radiation curve is identified. The precise nature of the radiator is then known in “our” reality. In quantum mechanics, this process is called decoherence. A macroscopic object is unlikely to be in a true quantum state as such states require very low temperatures, however the uncertainty in radiant flux can be interpreted using the Everett representation.

The modeling task therefore is to perform a controlled decoherence of a large number of possible co-existant states into a single macroscopic state.

This can be performed using linear algebra, and the idea of a quadratic form as the cost function defined on a solution manifold. The matrix representing the form controls the mixing of individual time streams (realities). [per the Everett interpretation]

Note that the gray body curve is smooth, in contrast to the curve of the selective radiator. An example of a selective radiator would be a metal such as copper which when reduced to powder and burned in an open flame produces a specific set of peaks corresponding to its valence energy levels.

 

The following Juypiter Notebooks file and latex writeup show a graphical implementation of these ideas in python 🙂

And the possible conclusions are startling!

 

Using Modeling and Simulation to solve Lambert’s problem to enable space travel

Spaceflight involves finding a path between an origin and a destination. In spaceflight mechanics, position and velocity are important because mathematically they determine the coordinates for both the origin and destination. The path between is called the orbit.

The determination of an orbit given two position vectors and the time of flight is known in celestial mechanics as Lambert’s problem, also known as two point boundary value problem. This contrasts with Kepler’s problem or propagation, which is rather an initial value problem.

The package poliastro.iod allows as to solve Lambert’s problem, provided the main attractor’s gravitational constant, the two position vectors and the time of flight. As you can imagine, being able to compute the positions of the planets as we saw in the previous section is the perfect complement to this feature!

For instance, this is a simplified version of the example Going to Mars with Python using poliastro, where the orbit of the Mars Science Laboratory mission (rover Curiosity) is determined:

ate_launch = time.Time(‘2011-11-26 15:02′, scale=’utc’)
date_arrival = time.Time(‘2012-08-06 05:17′, scale=’utc’)
tof = date_arrival – date_launch

ss0 = Orbit.from_body_ephem(Earth, date_launch)
ssf = Orbit.from_body_ephem(Mars, date_arrival)

from poliastro import iod
(v0, v), = iod.lambert(Sun.k, ss0.r, ssf.r, tof)

And these are the results:

>> v0
<Quantity [-29.29150998, 14.53326521, 5.41691336] km / s>
>>> v
<Quantity [ 17.6154992 ,-10.99830723, -4.20796062] km / s>

A Test of Einstein’s Theory

A recent study explored the understanding of relativity at an extra-galactic scale, which basically determines how gravity will behave over very large distances. To date, relativity remains the best description of gravitational attraction over long distances. To some, it was hoped that one day perhaps a shortcoming in the theory of relativity might point the way to a better understanding of the nature of the universe. As of yet, relativity has proved to be highly accurate. Now, a new study has opened up a new area of research.

The Hertsprung-Russel diagram showing